In the last step, the generated label data is used to create new corresponding image data. Thus, the label data that was produced by either mathematical modelling or by the WGAN is used as input to the trained pix2pix model. The resulting data pairs of label/image data can be used to train further networks for the actual segmentation task. The whole system is shown in Fig. REF . For our experiments, we chose a U-Net architecture to perform this segmentation task. It has to be emphasized that the selection of this specific network was done for the purpose of evaluating and comparing our augmentation approaches, and that the authors don't claim that architecture to be the best choice for the respective task. However, U-Net could achieve promising results in related fields like biomedical image segmentation [1]}.
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